Climate change and anthropogenic nutrient enrichment are driving the proliferation of algal blooms worldwide, posing a significant threat to aquatic ecosystems, including urban reservoirs. As water security in these systems is critical, inadequate monitoring continues to contribute to water quality deterioration and the loss of vital ecosystem services. This study aims to develop early detection tools for harmful algal blooms using Sentinel-2 multispectral imagery, the Random Forest algorithm, and in situ data. Model performance was evaluated for Trophic State Index (TSI), Chlorophyll-a (Chl-a), and surface water temperature in urban reservoirs, with initial calibration and validation on the A Baxe reservoir, followed by external validation in As Forcadas (northwest Spain). Spectral indices – such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Brightness Index (BI) – and specific bands were derived using Google Earth Engine (GEE). The developed models demonstrated high accuracy, with coefficients of determination (R2) of 0.97 for Chl-a, > 0.80 for TSI, and > 0.70 for temperature. The TSI model’s relevance is underscored by its integration of various physicochemical parameters (Ntotal, Ptotal, Secchi Disk, Chl-a), and the BI index showed strong performance in estimating temperature despite Sentinel-2′s lack of a thermal band. This study proposes transferable methodologies for any reservoir, delivering an effective tool for the early detection of harmful algal blooms in stagnant waters, and contributing significantly to improved water resource management and the protection of aquatic ecosystems.
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